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Do you want to know what the future may bring? Predictive analysis tools have an answer. Are they right? Sometimes. But sometimes can often be more than enough if the prediction can help your enterprise plan better, spend more wisely, and deliver more prescient service for your customers.

What are predictive analytics tools?

Predictive analytics tools blend artificial intelligence, data analysis, statistical modeling, and reporting to forecast future trends. The tools include sophisticated pipelines for gathering data from across the enterprise, add layers of statistical analysis and machine learning to make projections about the future, and distill these insights into useful summaries so that business users can act on them.

The quality of predictions depends on the data that goes into the system 鈥� the old slogan 鈥済arbage in, garbage out鈥� still holds today. But there are deeper challenges because predictive analytics software can鈥檛 anticipate moments when the world shifts gears and the future bears little relationship to the past. Still, the tools, which operate largely by ascertaining patterns, are growing increasingly sophisticated.

Working with dedicated predictive analytics tools is often relatively easy, at least compared to programming your own from scratch. Most tools offer visual programming interfaces that enable users to drag and drop icons optimized for data analysis. It helps to understand coding and to think like a programmer, but the tools make it possible to generate sophisticated predictions with a few mouse clicks. If you need more, adding custom code can solve many common issues.

What to look for in a predictive analytics tool?

The best place to begin is to look for a product that works with your data. All predictive analytics tools can analyze data in generic formats such as CSV, but many tools get along better with those from the same vendor. IBM鈥檚 SPSS, for instance, can work directly with the company鈥檚 db2 database. Cloud tools such as those from Amazon Web Services tend to be integrated with AWS鈥檚 many data storage solutions, like S3 or RDS.

Beyond the data, another key differentiator is the types of questions you intend to ask. Some tools are better at analyzing certain questions than others. Make sure the tool can compute the statistical measures needed to answer the questions your business needs to address.

Users must also be honest about their need for artificial intelligence. The area is exciting and new, but not every stack needs AI. A company that鈥檚 just asking for a simple number to predict demand for widgets next quarter doesn鈥檛 need a generative AI solution that may even hallucinate.

Another important question: Who will be using the tool? Some enterprises maintain teams of data scientists who want to develop new algorithms and work with open-source tools. They鈥檒l want more accessible stacks with the ability to integrate new code written in Python or R.

Other companies may just be starting to explore the power of predictive analytics and don鈥檛 need more than the standard routines. Here, tools that integrate quickly and offer analysis via existing modules are a better bet.

Still others may put a premium on ease of access for all users. For them, low-code and even no-code support makes all the difference. Paying attention to the options for customization can make a significant difference for your users.

Focused predictive analytics tools

Most of this article describes the bigger, more general tools applicable for any data analytics problem. Some vendors target smaller, more focused markets with specialized products optimized for the market鈥檚 needs. They can be more useful for enterprises in that business.

Some examples include:

• ChAI: Raw materials pricing is important for commodity sellers and buyers. offers AI-driven predictive analytics that follow these markets.
• OneModel: Managing humans is never easy. offers analytics tools for human resources to help understand and, maybe, predict what human workers will do.
• Hanzo Illuminate: Lawyers managing complex discovery work for litigation can turn to to analyze the emails, documents, and other workplace data.聽
• BlueOptima: Objective metrics for measuring the success of software development can help managers. offers a tool that can track the productivity of coders.
• Adobe Analytics: Understanding the cost and value of digital advertising is made a bit easier with , which can track channels, products, and services.
• ChannelMix: Marketing teams can turn to , which was 鈥渕ade by marketers, for marketers.鈥� The analytics focus on finding where the marketing budget will have the most impact.
• Hanarasoft: Maintenance teams can turn to for software that watches data streams for indications that some assets are going to need fixing.

Key questions when selecting a predictive analytics tool

• Who will be using it? Some teams want to democratize access to many employees. Other teams want to restrict access to a small group. The licensing costs and access mechanisms are very different.
• Are specialized AI algorithms needed? Some products are driven by companies on the bleeding edge of AI. Others are old-line statistical modeling solutions. The price and complexity are different, not just for the software but the hardware that runs it.
• Does your company have a unified data solution, like a warehouse or lake? If so, is there a tool that鈥檚 already integrated with your data curation stack?
• What type of reporting is needed? Some tools evolved from the business intelligence world that was focused on delivering tables of numbers in spreadsheets. Others began in the AI world and those features were added later.
• Is there a need for specialized graphics? Data visualization is another factor. Some applications require elaborate charts and graphs in beautiful dashboards. Others just want a table of trustworthy numbers.
• Are there issues for data access and governance? Some applications handle information that must be tightly controlled. Others are aimed at sharing data as widely as possible. Some tools offer more elaborate access mechanisms than others to meet data governance needs.
• Will non-coders be using the system? Some tools specialize in offering options so any end user can produce the reports or charts without waiting for developer time. Others cater to high-end data scientists who know how to code.
• Is the deployment model compatible? Some enterprises demand on-premises installations. Others love cloud flexibility. Do the options match the company鈥檚 culture and fit with the locations of the data?
• Are the costs predictable and manageable? Does the licensing model fit the way the data will be used? Are there per-user costs that are reasonable?
• Are there hidden costs? Sometimes a system will only work if the data is converted into a different format and this conversion is especially expensive. Sometimes a system might require special training.

Top predictive analytics tools compared

Alteryx Analytics Process Automation

The goal of Alteryx鈥檚 is to help you build a pipeline that cleans data before applying the best data science and machine learning algorithms. In goes your raw, sometimes messy data, and out appears reports, charts, and analysis for data-driven decisions. A high level of automation encourages deploying these models into production to generate a constant stream of insights and predictions. The visual IDE offers more than 300 algorithms, tools, and AI models that can be joined together to form a complex pipeline. One of the strengths of APA is its collection of deep integration with other data sources, such as geospatial databases or demographic data, to enrich the quality of your own data set.

Highlights:

• A very good solution for data scientists who must automate a complex collection of data sources to produce multiple deliverables
• Open design principles encourage 鈥渟elf-service for all鈥� through a mixture of low-code features and co-pilots
• For deployment locally or in the Alteryx cloud
• Includes AiDIN, Alteryx brand name for their generative AI and machine learning models tuned for data analytics
• Designed to drive insights to multiple customers who might want data presented as 鈥渕agic documents,鈥� 鈥減laybooks,鈥� dashboards, spreadsheets, or some other custom platform
• for tools such as the Designer starts at $5,195 per user for the desktop and $4,950 for the cloud version; many extras are priced by the sales team; free trials and are available

AWS SageMaker

One of Amazon鈥檚 main AI platform is well-integrated with the rest of the AWS fleet so you can analyze data from one of cloud vendor鈥檚 major data sources (SQL, NoSQL, S3, etc.) and then deploy it to run either in its own instance or as part of a serverless lambda function. is a full-service platform with data preparation tools such as the Unified Studio which brings together analytics, data processing, AI model development, and more under one umbrella. Data curation options for governance and security encourage creating a data lake for long-term work.

Highlights:

• Full integration with many parts of the AWS ecosystem makes this a great option for AWS-based operations
• Serverless options for deployment allow costs to scale with usage
• A marketplace facilitates buying and selling models and algorithms with other SageMaker users
• Integration with various AWS databases, data lakes, and other data storage options make working with big datasets simple
• is a la carte for the many different options and generally tied to the size of the computing resources used to support your calculations; a generous makes it possible to experiment

eQube Augmented Data Analytics

The data curation stack from eQ builds a data fabric from the various enterprise data sources. The offers a rich collection of algorithms that mix statistics and AI to produce predictive models that provide real-time insights and forecasts with their time-series analysis The AI system also includes generative models for better human-readable reporting.

Highlights:

• The full stack includes tools for handling data migration, cleansing, and automated reporting
• Governance options help secure collaboration
• Analytic engines include basic analytics as well as machine learning for building predictive models
• Pricing available from the sales team

H2O.ai AI Cloud

Turning good artificial intelligence algorithms into productive insights is the main goal of . It is first and foremost an AI company that looks for ways to help companies with their workflow. One of its best software agents recently in rankings of the very competitive GAIA benchmark. The company offers a collection of open-source and proprietary AI tools for a wide range of tasks such as classification, prediction, or creating聽 generative solutions. The software can run either in the H20.ai cloud or on-premises.

Highlights:

• An AI-driven stack offers a wide range of solutions including many beyond predictive analytics
• Tools range from AI Cloud for creating large, data-driven pipelines to open source, Python-based that helps desktop users create real-time dashboards
• Runs natively on premises or in any cloud
• A wide range of open-source models and tools nurture experimentation
• Pricing for enterprise support and cloud options available from the sales team

IBM SPSS

Statisticians have been using to crunch numbers for decades. The latest version includes options for integrating newer approaches such as machine learning, text analysis, or other AI algorithms. The Statistics package focuses on numerical explanations of what happened. is a drag-and-drop tool for creating data pipelines that lead to actionable insights.

Highlights:

• Ideal for large, traditional organizations with big data flows in traditional data lakes or database
• Tighter to R and Python encourage collaboration with open source tools
• Integrated with many other IBM tools such as
• Leverages larger initiatives such as IBM鈥檚 push for Trustworthy AI
• begins at $499 per user, per month, with generous free trials; other combinations available from the sales team

Altair RapidMiner

The tools from were always pitched first to the data scientists on the front lines. The core offering is a complete visual IDE for experimenting with various data flows to find the best insights. The product line now includes more automated solutions that can open the process to more people in the enterprise through a simpler interface and a guided series of tools for cleaning the data and finding the best modeling solution. These can then be deployed to production lines. The company has also been expanding their cloud offerings with an AI Hub designed to simplify adoption.

Highlights:

• Great for data scientists who are working directly with the data and driving exploration
• The Hyperworks tool encourages imaging the future through simulating potential solutions
• Offers transparency for users who need to understand the reasoning behind predictions
• Collaboration between AI scientists and users is encouraged with Jupyter notebook driven AI Hub
• Strong support for Python-based open-source tooling
• Broad provides RapidMiner Studio, Altair One, and other tools for early experimentation and educational programs
• Older versions and components as open source
• Pricing for larger projects and production deployment available by request

Microsoft

The breadth and depth of predictive analytics tools available has grown dramatically as their emphasis on the cloud, AI, and business intelligence has converged. Many parts of their ecosystem now include the ability to train models, perform statistical analysis, and generate predictions. The Dynamics 365 platform for tracking customers and managing resources a number of out-of-the-box predictive models. The Azure cloud offers tools for and other APIs delivering other AI services such as translators. Some of their models (like Phi3) and tools are open source. Generous free tiers are an introduction to many of the various services with a la carte pricing.

Highlights:

• Instead of building one flagship product, Microsoft has merged their various statistical and AI-based algorithms into many different products
• Some may start with Excel which now offers various predictive add-ons
• Their is a drag-and-drop tool for building and training models
• Others may turn to the cloud where various proprietary and open source LLMs can add predictive analytics
• Pricing is generally 鈥渃onsumption-based鈥�

SAP

Anyone who works in manufacturing knows SAP software. Its databases track our goods at all stages along the supply chain. So it should come as no surprise that they鈥檝e invested heavily in developing for predictive analytics to enable enterprises to make smarter decisions about what may be coming next. The information from the past informs the decisions about the future, mainly using a collection of machine learning and generative AI routines that are highly optimized for the general business questions. The mixture of generative AI鈥檚 ability to understand queries allows the tool to answer complicated questions about any data that鈥檚 in the system, not just the values that are on the standard data dashboards.

Highlights:

• Great for stacks that already rely on deep integration with SAP鈥檚 warehouse and supply chain management software
• Integrated with generative AI and machine learning to offer low-code and no-code strategy that open data to all
• Part of a regular business intelligence process for consistency and simplicity
• Users can drill deeper by asking for context behind the predictions to understand how the AI made the decision
• The company鈥檚 new open source offers some components as well as integration with and support for major projects
• Basic start at $300 per user, per year; a free plan allows experimentation; more capable plans with more automation and integration available from the sales team

SAS

One of the oldest statistics and business intelligence vendors around, SAS has grown stronger and more capable with age. Companies that need forecasting can produce forward-looking reports that depend on any mixture of statistics and machine learning algorithms, something SAS calls 鈥渃omposite AI.鈥� Its main product line, , is a general data curation and analytics powerhouse merging classical statistical approaches with more modern machine learning toolkits. Integration with open-source options such as Jupyter notebooks driven by Python allow wide experimentation. Specialized for challenges like network analysis or machine vision can drive particular use cases.

Highlights:

• A great collection of focused tools already optimized for specific industries such as banking or marketing
• Excellent merger of traditional statistics and modern machine learning
• with open-source options such as Jupyter notebooks for access to latest open research聽
• Pricing depends heavily on the product choice and the usage

Spotfire

If a picture is worth a thousand words, Spotfire鈥檚 goal is to create data-driven images that are worth uncountable words. The product鈥檚 main focus is creating elaborate, interactive visual presentations from data. Underneath, the analytics in the Statistica module can generate predictions that add to the main vision. The result can be a visual buffet filled with places to click that trigger subroutines called Action Mods. Modern machine learning adds more insight.聽

Highlights:

• A solution that produces rich visualizations that can unlock the trends in your data
• A tactile and interactive solution for all users
• Includes which embraces open-source options such as Python or R for crafting analytics routines
• Pricing available through the sales team

TIBCO

After data is gathered by various integration tools, TIBCO鈥檚 predictive analytics can start generating forecasts. The is designed to enable teams to work together to create low-code and no-code analytics. Developers can leverage classic data science approaches in R, Python, PySpark, and more. A more accessible tool called creates dashboards by integrating location-based data with historical results. The tools work with the company鈥檚 larger product line designed to support data gathering, integration, and storage.

Highlights:

• Great for supporting a larger architecture for data management
• The predictive analytics integrates with several data movement and storage options
• Builds on a tradition of generating reports and business intelligence
• Machine learning and other AI options can improve accuracy
• Some components are now being via open-source licenses as well as libraries for integrating with popular open-source stacks
• Pricing or cloud and on-premises options available directly from sales team